In the evolving world of financial modeling, even the best-built models can struggle to keep up with changing business needs, data, and user demands. Customers of modeling solutions often encounter a set of recurring issues, from performance slowdowns to disorganized inputs. Here, we look at five common modeling problems and how modelers can address them.

1. File is Too Slow or Large

Problem
A large, slow model can be a productivity killer, with calculations taking multiple seconds—or even minutes—whenever changes are made. Models that have been expanded repeatedly over time tend to accumulate unused features, redundant formulas, and excessive calculations, making the file practically unusable. Clients often hesitate to delete any parts of the model, fearing it might be essential to other areas of the model operating correctly. 

Solution
In most cases, improving model performance requires a rebuild. Start by removing unnecessary features and consolidating calculations to minimize redundancy. Choose formulas specifically optimized for speed in large files and be mindful about your use of circular references wherever possible, as they are common culprits of slowdowns. With strategic optimization, in most cases it’s possible to reduce calculation times to near-instantaneous.

2. Lack of Automated Scenario Functionality

Problem
Many clients have multiple versions of the same model to represent different scenarios, each with slight variations in assumptions. Managing multiple files like “Company Model_Base Case.xlsx” or “Company Model_High Case_StatusQuo_Debt Refi.xlsx” becomes a tedious task, sometimes costing clients hours when updating common assumptions across all files. This process is not only time-consuming but can also lead to version-control issues, with the risk of missing updates or making errors under time pressure.

Solution
To simplify scenario management, your goal as a modeler should be to centralize all input variations within a single model. Create dedicated input sections to store different scenario data, and use toggles or dropdowns to control which variations are active in the model – this allows users to seamlessly switch between scenarios and add new ones without needing to rebuild formulas. Additionally, consider incorporating dynamic outputs that display results for each scenario side-by-side; it helps users quickly review and compare various outcomes.

3. Disorganized Inputs, Calculations, and Outputs (Messy Design)

Problem
A poorly organized model can be a nightmare to navigate. Models that attempt to “do it all” often end up disorganized, with overlapping inputs, complex calculations squeezed into outputs, and a general lack of clarity. Unclear models make it challenging to know where inputs can be adjusted or where calculations are happening, often requiring an advanced understanding of the model just to make small changes.

Solution
Implement modeling best practices to create a clean, logical structure – separate your inputs, calculations, and outputs. Color-code inputs for easy identification, use simple formulas where possible, and ensure that each function of the model has its own dedicated section or tab. By improving readability, and legibility – you improve the model’s usability and transparency – giving users confidence to change, improve, and work with your model.

4. Manual Updates to Roll Model Forward Each Period

Problem
Clients often face a tedious manual update process for models that need to be updated each month, quarter, or year. This might include overriding formulas with actual results, inserting new columns, or adjusting prior period assumptions, all of which carry a high risk of user error. The manual nature of these updates can also lead to a loss of historical assumptions, making it difficult to track past model versions.

Solution
Consider automating the roll-forward process by setting up a centralized area for actual results and establishing input sections for archived assumptions. Use date switches to control which data sources the model pulls from for each period, and incorporate dynamic formulas that adjust based on the date switch. This setup allows users to update the model with minimal effort while preserving prior period data and assumptions. With automation, the model can stay up-to-date without extensive manual intervention, reducing the risk of errors and improving overall efficiency.

5. Model Lacks Desired Functionality

Problem
The model was built for a specific purpose and served that need well initially, but new functionality is needed as the client’s requirements evolve. The client may be proficient in Excel, but might lack the time or expertise to add these features without compromising the model. There is a fear of “breaking” the existing functionality.

Solution
As the modeler, approach the problem by first reviewing the scope of your client’s request. Your first goal should be to determine if the underlying model can support the requested change or if a full model rebuild is necessary. No matter the result, when adding additional functionality, you should always seek to build in a modular way. Separate your new work from past work where possible so it’s easier to integrate future updates without affecting other areas of the model.

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